The problem
Invoices, KYC documents, and contracts arrive as PDFs and photos; people retype them into systems. Pure-LLM demos look great and then quietly misread totals, dates, and names in production.
The system
A document pipeline combining layout-aware parsing with LLM extraction, per-field confidence scores, and validation rules (totals sum, dates parse, IDs checksum). Fields above threshold flow straight through; the rest queue for a human with the source region highlighted.
How it's built
- Ingestion for PDF/scan/photo; layout and table structure preserved
- Field-level confidence + business-rule validation before any write
- Hand-labeled eval set from your real documents; accuracy tracked per field per release
- Review UI that teaches the system: corrections become eval cases
Delivery
The Sprint runs on a few hundred of your real documents and reports per-field accuracy honestly; Build productionizes.
What to expect
- Straight-through processing for the clean majority of documents
- Error rates known per field — not discovered downstream
- Ops time shifts from typing to exception review
Documented results in the wild
Independent, published deployments of this class of system — cited as market evidence that it works at scale. These are not our clients.
- Zurich Insurance AI review of injury-claim paperwork cut processing time from one hour to five seconds and saved 40,000 work hours. Insurance Journal / Reuters, 2017 ↗
- Fukoku Mutual Life Document-reading AI for policy payouts delivered a 30% productivity increase, paying for itself within two years. Fortune, 2017 ↗